CN111563932A

CN111563932A - Overlapping coaxial surgical operation control method, system and readable storage medium

Info

Publication number: CN111563932A
Application number: CN202010421614.2A
Authority: CN
Inventors: 张希兰; 刘珊珊
Original assignee: Suzhou Liwei Xinpu Biotechnology Co ltd
Current assignee: Changzhou Second Peoples Hospital
Priority date: 2020-05-18
Filing date: 2020-05-18
Publication date: 2020-08-21
Anticipated expiration: 2040-05-18
Also published as: CN111563932B

Abstract

The invention relates to an overlapping coaxial surgical operation control method, a system and a readable storage medium, comprising the following steps: acquiring image information, acquiring case signals, obtaining case information, and establishing a preset model; analyzing a preset model to obtain an operation area, calculating an operation processing mode through big data to obtain operation information, obtaining a manipulator signal according to the operation information, analyzing manipulator state information, obtaining an overlapping area, and obtaining overlapping information; fitting the overlapped area and the operation area to obtain an area deviation rate, acquiring correction information when the area deviation rate is greater than a preset threshold value, and correcting the state information of the manipulator according to the correction information; the operation information is transmitted to the controller through the sensing nodes, and the controller controls the manipulator to carry out adaptive operation.

Description

Overlapping coaxial surgical operation control method, system and readable storage medium

Technical Field

The invention relates to the field of intelligent control or medicine, a blockchain technology and big data processing, in particular to a method and a system for controlling an overlapped coaxial surgical operation and a readable storage medium.

Background

The block chain (Blockchain) is an important concept of the bitcoin, is essentially a decentralized database and is a series of data blocks which are generated by correlation by using a cryptography method, each data block contains information of a batch of bitcoin network transactions and is used for verifying the validity (anti-counterfeiting) of the information and generating a next block, and the core and the foundation of the Internet of things are still the Internet and are extended and expanded networks on the basis of the Internet; and secondly, the user side extends and expands to any article to perform information exchange and communication, namely, the article information. The thing networking is through communication perception technologies such as intelligent perception, recognition technology and pervasive computing, uses thing networking and block chain technique to operation control system in, through big data analysis, automatic positioning operation position, then passes through thing networking automatic identification, transmission with operation data, then carries out permanent save to operation data through the block chain, when guaranteeing data security, can improve data transmission's ageing again.

Disclosure of Invention

The present invention overcomes the deficiencies of the prior art and provides a method, system and readable storage medium for controlling overlapping coaxial surgery.

In order to achieve the purpose, the invention adopts the technical scheme that: a method of controlling an overlapping coaxial surgical procedure, comprising:

acquiring image information, acquiring case signals, obtaining case information, and establishing a preset model;

analyzing a preset model, acquiring an operation area, and calculating an operation processing mode through big data to obtain operation information;

acquiring a manipulator signal according to the operation information, analyzing the manipulator state information, and acquiring an overlapping area to obtain overlapping information;

comparing the overlapping area with the operation area to obtain an area deviation rate;

judging whether the area deviation rate is greater than a preset deviation rate threshold value or not;

when the area deviation rate is larger than a preset deviation rate threshold value, acquiring correction information, and correcting the state information of the manipulator according to the correction information;

the operation information is transmitted to the controller through the sensing nodes, and the controller controls the manipulator to carry out adaptive operation.

Preferably, the preset model includes a convolutional neural network model, specifically:

acquiring patient image information, and obtaining patient data through cloud computing;

inputting data into a convolutional neural network model, training the convolutional neural network model through big data, and acquiring feedback information;

and positioning the surgical site according to the feedback information, and controlling the preset mode action of the manipulator by the controller to adjust the overlapping information.

Preferably, the method includes acquiring a manipulator signal according to the surgical information, analyzing the manipulator state information, acquiring an overlapping region, and acquiring overlapping information, and specifically includes:

respectively acquiring a first guide piece action signal and a second guide piece action signal, and respectively recording the signals as a first signal and a second signal;

calculating deflection angles of the first guide piece and the second guide piece, recording the deflection angles as a first angle and a second angle, and acquiring initial state information of the manipulator according to the deflection angles to obtain initial overlapping information of the manipulator;

controlling the action of the manipulator according to the operation information to acquire real-time overlapping information of the manipulator;

and performing de-marginalization processing on the real-time overlapping information.

Preferably, the initial position information of the sickbed is obtained, and the moving signal is obtained according to the operation information to obtain the moving information;

the position of the sickbed is adjusted by moving information, the image information of the patient is collected,

comparing the patient image information with the standard image information to obtain deviation information,

and acquiring a feedback signal according to the deviation information, and adjusting the position parameter of the sickbed according to the feedback signal.

Preferably, the processing the image information to obtain the result information specifically includes:

the image processing performs an amplification process at least by an amplifier;

segmenting the acquired image through a calculation unit, and extracting a characteristic value;

classifying the extracted characteristic values;

and storing the characteristic values of different classes to corresponding storage nodes through the gateway.

Preferably, the robot adaptability operation comprises one or more of moving, telescoping, rotating, tool head changing and sterilizing of the robot.

The second aspect of the present invention also provides an overlapping coaxial surgical control system, characterized in that it comprises: a memory including an overlapping coaxial surgical control method program, the overlapping coaxial surgical control method program when executed by the processor implementing the steps of:

A third aspect of the present invention provides a computer-readable storage medium characterized by: the computer readable storage medium includes a program of an overlapping coaxial surgical control method, which when executed by a processor implements the steps of the overlapping coaxial surgical control method of any one of the above.

The invention solves the defects in the background technology, and has the following beneficial effects:

(1) the invention is based on the block chain technology, the whole operation process is decentralized processing, the safety of user data is higher, the operation robot can automatically identify the operation part through big data analysis and processing according to the collected image information, the misoperation of the operation is avoided, and the accuracy of the operation is improved.

(2) Through collection, analysis patient image information, extract the eigenvalue, through big data analysis case information, location patient operation region, then through the thing networking with data transmission to controller, the controller control two or the operation region of a plurality of manipulators overlap, and the operation of a plurality of manipulators linkage can be realized to the overlap district, can carry out complicated operation.

(3) The model is generated by collecting and analyzing big data of historical patients, the model can be generated in a neural network mode, when a new patient performs surgery, patient information and image information are collected and input into the neural network model, the model automatically calculates the surgery position, and the surgery accuracy is improved.

(4) The neural network model is trained through big data, so that the neural network model is more and more accurate, the state change of the manipulator is controlled through the neural network model, and the action information of the manipulator is matched with the action information of medical staff.

(5) Operation information is intelligently sensed and identified through the Internet of things, and then block chains are permanently generated by identification data, so that the safety of the system and the rapidity of data identification are improved.

Drawings

The invention is further illustrated with reference to the following figures and examples.

FIG. 1 illustrates a flow chart of a method of controlling an overlapping coaxial surgical procedure of the present invention;

FIG. 2 illustrates a neural network model method flow diagram;

FIG. 3 shows a flow chart of a method of obtaining overlapping information.

Fig. 4 shows a flow chart of a method for adjusting a patient bed position parameter.

FIG. 5 illustrates a block diagram of an overlapping coaxial surgical control system.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.

FIG. 1 shows a flow chart of a method of controlling an overlapping coaxial surgical procedure according to the present invention.

As shown in FIG. 1, a first aspect of the present invention provides a method of overlapping coaxial surgical control, comprising:

s102, collecting image information, acquiring case signals, obtaining case information, and establishing a preset model;

s104, analyzing a preset model, acquiring an operation area, and calculating an operation processing mode through big data to obtain operation information;

s106, acquiring a manipulator signal according to the operation information, analyzing manipulator state information, and acquiring an overlapping area to obtain overlapping information;

s108, comparing the overlapping area with the operation area to obtain an area deviation rate;

s110, judging whether the deviation rate of the area is greater than a preset deviation rate threshold value or not;

s112, when the area deviation rate is larger than a preset deviation rate threshold value, acquiring correction information, and correcting the state information of the manipulator according to the correction information;

and S114, transmitting the operation information to a controller through the sensing node, and controlling the manipulator to perform adaptive operation by the controller.

It should be noted that, the two guiding parts are arranged to guide the mechanical arm, the two guiding parts are overlapped positively, that is, the centers of the two guiding parts are connected through a rotating shaft, the two guiding parts can be matched with each other in the rotating process to form a single spherical operation area, the two guiding parts can also be overlapped without centers, that is, the rotating shaft is connected with the center position of one guiding part and connected with the non-center position of the other guiding part, so that the axial position change and the included angle change of the guiding parts are realized, the two guiding parts are matched with each other in the rotating operation process to form a double spherical operation area, the operation flexibility is improved, the overlapped area is the operation overlapped area of the two mechanical arms on the same guiding part or the operation overlapped area of two or more mechanical arms on different guiding parts, the multi-mechanical arm linkage operation can be carried out in the overlapped area, the image information collected by the camera is obtained according to the image information, case information can generate a block chain, data in the block chain are subjected to centralization processing and encryption processing, the safety of the data is guaranteed, original case history data are analyzed through big data, the original case history data are input into a model, an operation area can be automatically positioned, a manipulator is controlled through a controller to perform operation, signals and data are transmitted through the Internet of things in the operation process to perform intelligent identification and transmission of the signals, and the rapidity of signal transmission is improved.

As shown in FIG. 2, the present invention discloses a neural network model method flow diagram;

according to the embodiment of the present invention, the preset model includes a convolutional neural network model, which specifically includes:

s202, acquiring image information of a patient, and obtaining patient data through cloud computing;

s204, inputting data into a convolutional neural network model, training the convolutional neural network model through big data, and acquiring feedback information;

and S206, positioning the surgical site according to the feedback information, and controlling the preset mode action of the manipulator by the controller to adjust the overlapping information.

It should be noted that, train the neural network model through big data, make the neural network model more and more accurate, control manipulator state change through the neural network model, make manipulator action information and medical staff action information phase-match, through gathering, analysis patient image information, extract the eigenvalue, through big data analysis case information, location patient operation region, then through thing networking with data transmission to controller, the controller controls the operating region of two or more manipulators and carries out the overlap, the overlap district can realize a plurality of manipulator linkage operations, can carry out complicated operation.

As shown in FIG. 3, the present invention discloses a flow chart of a method for obtaining overlapping information;

according to the embodiment of the invention, acquiring the manipulator signal according to the operation information, analyzing the manipulator state information, acquiring the overlapping area, and acquiring the overlapping information specifically comprises the following steps:

s302, respectively acquiring a first guide piece action signal and a second guide piece action signal, and respectively recording the signals as a first signal and a second signal;

s304, calculating deflection angles of the first guide piece and the second guide piece, recording the deflection angles as a first angle and a second angle, and acquiring initial state information of the manipulator according to the deflection angles to obtain initial overlapping information of the manipulator;

s306, controlling the action of the manipulator according to the operation information to acquire real-time overlapping information of the manipulator;

s308, performing de-marginalization processing on the real-time overlapping information.

The guiding assembly comprises a first guiding part and a second guiding part, the first guiding part and the second guiding part are movably connected through a rotating shaft, the rotating shaft respectively penetrates through the first guiding part and the second guiding part, when the rotating shaft is connected with the center positions of the first guiding part and the second guiding part, positive overlapping is realized, at the moment, two or more mechanical arms are matched to realize a spherical operating surface, and a camera is arranged at the bottom of the rotating shaft and can acquire image information; the first guide piece is all connected with a plurality of manipulator overlap areas with the second guide piece inboard in a matched manner and is the manipulator operation coincidence region, when the overlap area deviates, the overlap area position or the overlap area size is adjusted through adjusting the manipulator position or adjusting the guide piece angle.

As shown in fig. 4, the present invention discloses a flow chart of a method for adjusting the position parameters of a patient bed;

according to the embodiment of the invention, S402, the initial position information of the sickbed is obtained, and the movement signal is obtained according to the operation information to obtain the movement information;

s404, adjusting the position of the sickbed through the movement information, collecting image information of the patient,

s406, comparing the patient image information with the standard image information to obtain deviation information,

and S408, acquiring a feedback signal according to the deviation information, and adjusting the position parameter of the sickbed according to the feedback signal.

It should be noted that the guide rail is arranged at the bottom of the sickbed, the sickbed can move along the guide rail, deviation information is obtained by comparing the patient image with the standard image, when the deviation information appears, the deviation of the position of the sickbed is indicated, which is not beneficial to operation, the operation deviation is easily caused in the operation process, at the moment, the patient is in a reasonable operation area by adjusting the position of the sickbed, and the position parameter information of the sickbed comprises the position between the sickbed and the camera and the position between the sickbed and the guide rail.

According to the embodiment of the present invention, the processing the image information to obtain the result information specifically includes:

classifying the extracted characteristic values;

It should be noted that, the image information is amplified by the amplification processing unit, when the image feature value is extracted, the validity of the feature value is ensured, different blocks are generated by different types of feature values and are permanently stored, in the next patient operation process, data in the blocks are extracted, data comparison reference is performed, through feature value training, the model is gradually enabled to be faster and more accurate in the aspect of automatically identifying the operation part, the image is segmented by the operation unit, partial image features can be respectively extracted from the segmented image, the extracted features are integrated, and the feature value without reference is removed.

According to the embodiment of the invention, the manipulator adaptability operation comprises one or more of movement, expansion and contraction, rotation, tool head replacement and disinfection of the manipulator.

As shown in FIG. 5, the present invention discloses a block diagram of an overlapping coaxial surgical control system;

It should be noted that, the two guiding parts are arranged to guide the mechanical arm, the two guiding parts are overlapped positively, that is, the centers of the two guiding parts are connected through a rotating shaft, the two guiding parts can be matched with each other in the rotating process to form a single spherical operation area, the two guiding parts can also be overlapped without centers, that is, the rotating shaft is connected with the center position of one guiding part and connected with the non-center position of the other guiding part, so that the axial position change and the included angle change of the guiding parts are realized, the two guiding parts are matched with each other in the rotating operation process to form a double spherical operation area, the operation flexibility is improved, the overlapped area is the operation overlapped area of the two mechanical arms on the same guiding part or the operation overlapped area of two or more mechanical arms on different guiding parts, the multi-mechanical arm linkage operation can be carried out in the overlapped area, the image information collected by the camera is obtained according to the image information, case information can generate a block chain, data in the block chain is subjected to decentralized processing and encrypted processing to ensure the safety of the data, original case history data is analyzed through big data, the original case history data is input into a model to automatically position an operation area, a manipulator is controlled through a controller to perform operation, signals and data are transmitted through the Internet of things in the operation process to intelligently identify and transmit the signals, the rapidity of signal transmission is improved, image information of a patient is acquired and analyzed, characteristic values are extracted, case information is analyzed through the big data to position the operation area of the patient, then the data are transmitted to the controller through the Internet of things, the controller controls the operation areas of two or more manipulators to be overlapped, the overlapped area can realize linkage operation of the multiple manipulators, complex operation can be performed, and the guide assembly comprises a first guide piece 3 and a second guide piece, the first guide piece and the second guide piece are movably connected through a rotating shaft, the rotating shaft respectively penetrates through the first guide piece 3 and the second guide piece, a camera is arranged at the bottom of the rotating shaft, and the camera can collect image information; the first guide piece is all connected with a plurality of manipulator overlap areas with the second guide piece inboard in a matched manner and is the manipulator operation coincidence region, when the overlap area deviates, the overlap area position or the overlap area size is adjusted through adjusting the manipulator position or adjusting the guide piece angle.

The invention is based on the block chain technology, the whole operation process is decentralized processing, the safety of user data is higher, the operation robot can automatically identify the operation part through big data analysis and processing according to the collected image information, the misoperation of the operation is avoided, and the accuracy of the operation is improved.

Through collection, analysis patient image information, extract the eigenvalue, through big data analysis case information, location patient operation region, then through the thing networking with data transmission to controller, the controller control two or the operation region of a plurality of manipulators overlap, and the operation of a plurality of manipulators linkage can be realized to the overlap district, can carry out complicated operation.

The model is generated by collecting and analyzing big data of historical patients, the model can be generated in a neural network mode, when a new patient performs surgery, patient information and image information are collected and input into the neural network model, the model automatically calculates the surgery position, and the surgery accuracy is improved.

The neural network model is trained through big data, so that the neural network model is more and more accurate, the state change of the manipulator is controlled through the neural network model, and the action information of the manipulator is matched with the action information of medical staff.

Operation information is intelligently sensed and identified through the Internet of things, and then block chains are permanently generated by identification data, so that the safety of the system and the rapidity of data identification are improved.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method of controlling an overlapping coaxial surgical procedure, comprising:

2. An overlapping coaxial surgical control method according to claim 1, wherein: the preset model comprises a convolutional neural network model, and specifically comprises the following steps:

3. An overlapping coaxial surgical control method according to claim 1, wherein: according to operation information acquisition manipulator signal, analysis manipulator state information acquires the overlap region, obtains the overlap information, specifically includes:

4. An overlapping coaxial surgical control method according to claim 1, wherein: acquiring initial position information of a sickbed, and acquiring a moving signal according to operation information to obtain moving information;

5. An overlapping coaxial surgical control method according to claim 1, wherein: the image information is processed to obtain result information, and the method specifically comprises the following steps:

classifying the extracted characteristic values;

6. An overlapping coaxial surgical control method according to claim 5, wherein:

the adaptive operation of the manipulator comprises one or more of moving, stretching, rotating, tool head replacing and sterilizing of the manipulator.

7. An overlapping coaxial surgical control system, comprising: a memory including an overlapping coaxial surgical control method program, the overlapping coaxial surgical control method program when executed by the processor implementing the steps of:

8. The overlapping coaxial surgical control system of claim 7, wherein: according to operation information acquisition manipulator signal, analysis manipulator state information acquires the overlap region, obtains the overlap information, specifically includes:

9. The overlapping coaxial surgical control system of claim 8, wherein: the preset model comprises a convolutional neural network model, and specifically comprises the following steps:

10. A computer-readable storage medium characterized by: included in the computer readable storage medium is an overlapping coaxial surgical control method program which, when executed by a processor, implements the steps of the overlapping coaxial surgical control method according to any one of claims 1 to 6.